Improving alternative text clustering quality in the avoiding bias task with spectral and flat partition algorithms

Authors:
M. Eduardo Ares;Javier Parapar;Álvaro Barreiro
Affiliations:
IRLab, Department of Computer Science, University of A Coruña, Spain;IRLab, Department of Computer Science, University of A Coruña, Spain;IRLab, Department of Computer Science, University of A Coruña, Spain
Venue:
DEXA'10 Proceedings of the 21st international conference on Database and expert systems applications: Part II
Year:
2010

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Abstract

The problems of finding alternative clusterings and avoiding bias have gained popularity over the last years. In this paper we put the focus on the quality of these alternative clusterings, proposing two approaches based in the use of negative constraints in conjunction with spectral clustering techniques. The first approach tries to introduce these constraints in the core of the constrained normalised cut clustering, while the second one combines spectral clustering and soft constrained k-means. The experiments performed in textual collections showed that the first method does not yield good results, whereas the second one attains large increments on the quality of the results of the clustering while keeping low similarity with the avoided grouping.